基于马尔可夫过程的坦克火力随机对抗模型

根据小规模坦克火力对抗中所具有的马尔可夫性特点,将该过程视为离散状态、离散时间的马尔可夫随机过程(马尔可夫链),由此建立了坦克与反坦克武器系统之间一对一对抗的随机格斗模型,并给出双方获胜概率和平均对抗回合数的计算公式。最后通过实例验证了模型的有效性。该模型克服了轮流对抗不符合战场实际的缺点,为分队指挥员在射击策略的选择和分队火力运用上的快速决策提供了较为精确的量化依据。     

导管螺旋桨敞水性能的预报和比较

借助于计算流体力学软件,对导管螺旋桨的敞水性能进行了数值模拟,得到了在不同网格模型和湍流模型下导管螺旋桨的正车敞水性能曲线,通过与试验图谱的对比分析,发现Wilcoxk-ω模型更适合于导管螺旋桨的敞水性能计算,并且采用结构化网格和非结构化网格相结合的计算方法能够满足导管螺旋桨敞水性能预报的工程精度要求。同时,当网格中存在少量高度倾斜的网格单元时,在不影响计算收敛的情况下,仍能将计算误差控制在10%以内。此外,如果要获得更为精确的计算结果,应提高网格质量,尽量使用结构化网格,并将近壁面网格加密,合理控制壁面附近的Y+值。虽然网格数量的增加并不总是意味着计算误差的减少,但合理控制网格细密度能够获得更为可信的计算结果。     

具有全局收敛性的盲均衡算法

为了解决传统的自适应均衡技术需发送训练序列的问题,采用盲均衡技术,利用接收信号本身的特征使均衡器收敛。提出了一种新的盲均衡算法(C&D算法),该算法将CRIMNO算法和判决引导(Decision Directed,DD)算法相结合,利用信号序列间的互不相关特性,从而使其具有全局收敛性能。实验结果表明:与CRIMNO算法相比,在相同的信噪比下,该算法具有收敛快速、运算量少和残留误差小的特点。     

The orienteering problem

Orienteering is a sport in which start and end points are specified along with other locations. These other locations have associated scores. Competitors seek to visit, in a fixed amount of time, a subset of these locations on the way from the start point to the end point in order to maximize the total score. An effective center-of-gravity heuristic is presented that outperforms heuristics from the literature.     

Estimation from binomial data with classifiers of known and unknown imperfections

Observations from inspection by a “test” method and a standard method are combined to provide estimators of population proportion, and of probabilities of misclassification for the test method. Results of Hochberg and Tenenbein [3] and of Albers and Veldman [1] are extended to the case where the standard method is not perfect, but its misclassification probabilities have known values. Both moment and maximum-likelihood estimators are considered and some asymptotic properties of the resulting estimators are compared.